Princeton University researchers have developed the world’s first integrated silicon photonic neuromorphic chip, which contains 49 circular nodes etched into semiconductive silicon.

In Brief

Princeton University researchers have developed the world’s first integrated silicon photonic neuromorphic chip, which contains 49 circular nodes etched into semiconductive silicon.

The chip could complete a math equation 1,960 times more quickly than a typical central processing unit, a speed that would make it ideal for use in future neural networks.

Brain-Like Computers

As developments are made in neural computing, we can continue to push artificial intelligence further. A fairly recent technology, neural networks have been taking over the world of data processing, giving machines advanced capabilities such as object recognition, face recognition, natural language processing, and machine translation.

These sound like simple things, but they were way out of reach for processors until scientists began to find way to make machines behave more like human brains in the way they learned and handled data. To do this, scientists have been focusing on building neuromorphic chips, circuits that operate in a similar fashion to neurons.

Now, a team at Princeton University has found a way to build a neuromorphic chip that uses light to mimic neurons in the brain, and their study has been detailed in Cornell University Library.

Getty Images/Brand X

The Princeton University researchers developed the world’s first integrated silicon photonic neuromorphic chip. This optical computing device features 49 circular nodes etched into semiconductive silicon. Each of these “neuron-like” nodes works with a specific wavelength of light. The light rapidly circulates in the node, and when released, it affects the output of a laser. When the laser output returns to the nodes, it completes the circuit.

The researchers proved that the chip is capable of super-fast computing by demonstrating that it could crunch a mathematical differential equation 1,960 times more quickly than a typical central processing unit, which uses electrons.

Futuristic Devices and Machine Learning

The team at Princeton believes that their development can be easily adopted by the industry to bring optical computing into the mainstream for the first time. “Silicon photonic neural networks could represent first forays into a broader class of silicon photonic systems for scalable information processing,” researcher Alexander Tait told MIT Technology Review.